Staff Engineer, Data and AI Enablement

Key Outcomes:

• Partner with PMs to translate business and product opportunities and our shared strategic vision into scalable data and AI solutions, from early exploration through production rollout.

• Deliver reliable data products that support data and AI enabled features, experimentation, personalization and decision-making across the company.

• Build and scale batch and real-time pipelines that ingest, transform, and prepare high-quality data for reporting, machine learning training, model evaluation, feature generation, and production inference.

• Evolve the data and ML platform architecture across orchestration, storage, compute, streaming, and data access, using technologies such as Airflow, Kafka, Redshift, Glue, Vector DBs and other cloud native services.

• Improve data trust and usability by establishing strong practices for data modeling, schema evolution, data contracts, testing, lineage, privacy controls, freshness, and recoverability.

• Enable teams to work more independently by creating reusable tools, standards, and paved paths that make it easier to discover data and build dependable workflows.

• Maintain a resilient and efficient platform through observability, alerting, runbooks, incident response, on-call participation, performance tuning, and ongoing cost optimization.

• Raise the technical bar for data and AI infrastructure by leading architectural decisions, mentoring engineers, reviewing designs and code, reducing technical debt, and advancing the use of AI and agentic workflows.

• This job is expected to participate in our on call rotation

The most important core competencies for the role are:

• Distributed data systems expertise: Able to design and scale reliable batch and real-time data architectures.

• Strong software engineering judgment: Builds maintainable, testable production systems in Python and/or comparable languages.

• Data modeling and SQL expertise: Designs scalable, trustworthy data models and data products.

• Cloud and platform architecture: Makes sound trade-offs across compute, storage, orchestration, streaming, infrastructure, and cost.

• Operational excellence: Demonstrates strong operational ownership through observability, incident response, on-call participation, runbooks, performance tuning, and building resilient systems.

• AI engineering fluency and technical leadership: Understands modern AI and LLM infrastructure and leads through architecture, collaboration, mentoring, and influence.

Target experience:

• 7+ years of software engineering experience, with significant experience building distributed systems, data platforms, ML platforms, or comparable production infrastructure.

• Hands-on experience building and operating large-scale batch and/or streaming data systems, ideally including Kafka, Spark, workflow orchestration and similar technologies.

• Experience designing and operating cloud-native data infrastructure using technologies such as AWS/GCP, infrastructure as code, containers, orchestration, and managed data services.

• Experience taking data or ML/AI systems into production, including reliability, observability, deployment, evaluation, and operational ownership.

• Practical experience with modern AI infrastructure, such as embeddings/vector retrieval, LLM evaluation and observability, or agentic workflows.

This is a full time role based in the United States, Canada or Mexico.

The annual salary of a Staff Engineer, Data and AI is $236,000 in San Francisco and New York, and is $224,000 in Austin, Boston, Chicago, DC, LA and Seattle and is $200,500 in all other US locations.

The annual salary of a Staff Engineer, Data and AI is $221,500 CAD in Vancouver and Toronto, Canada, and is $205,500 CAD in Victoria and Calgary and is $202,000 CAD in all other Canadian locations.

The annual salary of a Staff Engineer, Data and AI is $1,727,000 MXN anywhere within Mexico.